Frequency-selective beamforming and single-shot beam training with dynamic metasurface antennas

Joseph Carlson; Miguel R. Castellanos; Nitish Vikas Deshpande; Robert W. Heath Jr

REVIEW 2 major objections 4 minor 32 references

Frequency-selective beamforming and single-shot beam training with dynamic metasurface antennas

T0 review · 2 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read By optimizing operating frequency, a DMA reaches true-time-delay N-squared gain, and a single OFDM pilot trains its beam.

desk verdict The two-stage frequency optimization is a solid, genuinely new result; the single-shot beam training rate plot overstates robustness because it assumes noiseless subcarrier selection. read the letter →

arxiv 2412.00215 v1 pith:NJRC3IKK submitted 2024-11-29 eess.SP

Nitish Vikas Deshpande , Joseph Carlson , Miguel R. Castellanos , Robert W. Heath Jr This is my paper · ORCID

classification eess.SP

keywords dynamicmetasurfaceantennasfrequency-selectivebeamformingsingle-shotbeamtrainingtrue-time-delayarraygainresonantfrequencytuningOFDMpilotsub-arraycodebook

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper shows that a dynamic metasurface antenna (DMA) transmitter can reach the same beamforming gain as a true-time-delay (TTD) array, $N^2$, by treating the operating frequency as an optimization variable along with the per-element resonant frequencies, and by designing the waveguide refractive index and element spacing for the desired angular coverage. The authors then exploit the same frequency-angle coupling to perform beam training in a single OFDM pilot symbol: sub-arrays of DMA elements point at different candidate angles on different subcarriers, the receiver reports the strongest subcarrier, and the array reconfigures to the corresponding optimal frequency. With only four resonant frequency states, the trained beam achieves a rate within 3 dB of the perfect-angle case. This matters because DMAs use low-power varactor or PIN diodes instead of power-hungry phase shifters or true-time-delay lines, so matching TTD performance at lower hardware cost would make wideband beamforming more energy-efficient.

What carries the argument

The engine is the Lorentzian polarizability model of each DMA slot, $\alpha_{M,n}(f) = F 2\pi f^2 / (2\pi f_{r,n}^2 - 2\pi f^2 + j\Gamma f)$, which couples the magnitude and phase of the tunable weight. The paper reparametrizes this constraint with a shift-of-origin transformation, $[f_{\rm DMA}(f)]_n = (-j + e^{j\tilde\psi_n(f)})/2$, turning the coupled problem into a unit-modulus beamforming problem with closed-form optimal phases (Lemma 1). The load-bearing identity is the array-factor term $S(\phi,f) = \sin(\pi N f d_y(n_g+\sin\phi)/c) / \sin(\pi f d_y(n_g+\sin\phi)/c)$, whose magnitude can equal $N$ when $p = f d_y(n_g+\sin\phi)/c$ is an integer; that condition links angle, frequency, waveguide index, and spacing, and it drives both the frequency-optimization lemma and the design rules for angular coverage. The beam-training mechanism is the same map read in reverse: different subcarriers carry different angles through $f_t^\star(\phi)$, so a sub-array codebook built from Lemma 5 lets one OFDM symbol sweep the whole angular range and report the best frequency.

What would settle it

Run the single-shot training with additive noise at the receiver at the SNR values of Fig. 10; if subcarrier selection errors push the post-training rate more than 3 dB below the perfect-AoD curve, the core claim that four frequency states suffice would fail. Alternatively, measure the beamforming gain at an angle midway between two adjacent codebook angles and check whether it falls below $\delta N_y^2 N_z^2$, which would contradict Lemma 5.

Watch

Extended reading notes

Core claim

The central claim is that the frequency selectivity of a DMA is not merely a source of beam-squint loss but a resource: by choosing the operating frequency $f_t$ jointly with the element resonant frequencies $\{f_{r,n}\}$, the beamforming gain at any angle $\phi$ in a designable range can be pushed to the TTD upper bound $N^2$. Lemma 2 gives the optimal frequency as $f_t^\star(\phi) = p^\star c / (d_y(\sin\phi + n_g))$, with $p^\star$ chosen to maximize the array factor $|\sin(\pi N p)/\sin(\pi p)|$; full gain is achieved exactly when an integer $p^\star$ lies in the feasible band, in which case every slot resonates at the operating frequency. Lemma 3 provides closed-form waveguide refractive index $n_g^\star$ and inter-element spacing $d_y^\star$ that make this possible for every angle in a specified interval $[\phi_{\rm lw}, \phi_{\rm up}]$. For the sub-array architecture, the paper proves that a single OFDM pilot symbol suffices: each sub-group of DMAs probes a different sector angle at its own optimal frequency, the receiver selects the subcarrier with maximum gain, and reconfiguring all elements to that frequency keeps the beamforming gain within a factor $\delta$ of $N_y^2 N_z^2$ over the whole sector (Lemmas 5 and the codebook recursion). The rate evaluation shows the trained configuration matches the perfect-AoD rate and tracks a TTD array up to roughly the DMA's 3 dB bandwidth.

Load-bearing premise

The single-shot beam training claim rests on the receiver identifying the strongest subcarrier without error; the paper does not model noise or estimation error, and during training only $Q = N_z/L$ sub-arrays point at the true direction, so the training-phase SNR is lower than in the final beamforming stage.

Editorial extensions

If this is right

A DMA designed with $n_g = n_g^\star$ and $d_y = d_y^\star$ achieves the $N^2$ TTD-level beamforming gain at every angle in the target sector, provided the operating band is wide enough to contain an integer $p^\star$ for each angle.
The optimal frequency strategy dominates the fixed-center-frequency DMA benchmark for every angle and every tuning range, with the gap shrinking as the tuning range grows (Figs. 4 and 11).
Four resonant frequency states suffice for both training and data transmission; continuous high-resolution tuning is not needed to stay within 3 dB of the ideal beamforming gain.
The DMA rate tracks the TTD rate up to about the 3 dB bandwidth of the gain response, roughly $\Gamma/(2\pi)$ in the element-limited regime, and falls off beyond it.
Binary (PIN-diode) DMA weights match continuous weights only at the single angle whose optimal frequency equals the center frequency; elsewhere they lose more than 2 dB because some slots are turned off.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the angle-to-frequency map is monotone and closed-form, the same resonant-frequency readout could in principle be used for passive angle-of-arrival estimation or for serving multiple users at different angles on different subcarriers without extra training, though the paper does not analyze multiuser or multipath settings.
The training analysis assumes error-free subcarrier selection. In a low-SNR training phase, selection errors will blur the angle estimate; a natural extension is to add a second pilot or to dither the sector frequencies to average out selection errors, which would test how much of the 3 dB margin survives.
The codebook recursion in Lemma 5 suggests a hardware-aware trade-off: for fixed total elements $N_y N_z$, narrower per-sector beams (larger $N_y$) require more sectors $L$ to cover the same range, so the number of resonant states can be traded against row length in a way the paper quantifies but does not optimize.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: the frequency-selective beamforming analysis is the real meat, and it holds up. The two-stage optimization—choosing the operating frequency, then the resonant frequencies—delivers a genuine N^2 gain over a designable angular range when the refractive index and spacing are designed via Lemma 3. That is new for DMAs and useful for wideband systems. The single-shot beam training idea is clever, but the paper overstates it: the rate comparison in Fig. 10 assumes the receiver picks the best subcarrier with no noise. During training only Q = Nz/L subarrays point at the true angle, so the pilot SNR is roughly 20 log10(L) lower than in the data stage. At sector edges adjacent beams are within the design threshold of each other, so a modest noise floor can flip the selection. The claim that four resonant states suffice for both training and beamforming is not yet established; Fig. 10 is an upper bound.

What is well done: Lemmas 1-4 are internally consistent, the Lorentzian model is used correctly, and the closed-form expressions are plausible. Lemma 3's design criterion for N^2 coverage is a real contribution. The paper is self-contained, the MATLAB code is linked, and the deterministic simulations validate the gain formulas, including the 3 dB bandwidth prediction. The comparisons against fixed-frequency and binary-weight benchmarks are fair and show the proposed approach wins across the tuning range. Self-citation of [2] is minor; other prior work is cited properly.

Soft spots beyond training noise: the analysis is ideal—LOS, no attenuation, single user—though they do show attenuation does not change the trend. The "limited frequency states suffice" conclusion is extrapolated from L = 4 examples; there is no general bound on L versus the gain threshold. That is a moderate gap, not fatal. Also, the operating-frequency optimization assumes the system can freely pick any frequency in the band, which may not be practical in a standards-based system, but the paper acknowledges this and motivates it via dynamic spectrum sharing.

Who this is for: anyone working on DMA signal processing, wideband beamforming, or low-cost TTD alternatives. It deserves a serious referee; the main theoretical result is significant enough to warrant review even though the training analysis needs major revision. My recommendation: send it to review, but require the authors to either analyze noisy subcarrier selection or explicitly frame the training rate as an upper bound.

Referee Report

2 major / 4 minor

Summary. This paper studies a single-user MISO system with a transmit dynamic metasurface antenna (DMA). The authors first address the frequency-selective beamforming gain maximization by jointly optimizing the DMA resonant frequencies and the operating frequency for a line-of-sight channel. They derive closed-form expressions for the optimal resonant frequencies (Lemma 1), characterize the gain achieved by optimizing the operating frequency (Lemma 2), and give designs for the waveguide refractive index and element spacing that extend the maximum-gain N^2 regime over a desired angular range (Lemma 3). They also derive the ν-bandwidth of the DMA frequency response (Lemma 4). The second contribution is a single-shot beam training scheme in which the DMA array is split into L subarrays, each tuned to a different sector angle and corresponding optimal frequency, and an OFDM pilot symbol is used to have the receiver select the best subcarrier; the selected frequency is then used for data transmission. Numerical simulations validate the gain analysis and compare the achievable rate of the proposed DMA configuration with fixed-frequency DMA benchmarks and true-time-delay arrays.

Significance. If the main claims hold, the paper offers a useful frequency-domain perspective on DMA beamforming: it shows that a DMA can match the N^2 beamforming gain of a true-time-delay array over a designable angular range by jointly choosing the operating frequency and the waveguide parameters, and that a finite set of resonant states can be used for both training and beamforming. The closed-form derivations in Lemmas 1–4 are internally consistent and are validated by deterministic simulations, and the MATLAB code is made publicly available. The frequency-selective gain analysis (Lemmas 2–3) is the strongest part of the paper. The beam training claim, however, is not yet fully established because the selection rule in Eq. (48) is assumed noiseless and the coverage analysis in Section IV-C is connected to the post-training gain rather than to the actual training-stage gain.

major comments (2)

[IV-B, Eq. (48)] The single-shot beam training claim rests on the assumption that the receiver selects the best subcarrier without noise or estimation error. The paper does not model the training-stage SNR: during the pilot transmission only Q = Nz/L subarrays are configured near the true angle, so the coherent pilot energy at the best subcarrier scales as (Q Ny)^2 rather than (Nz Ny)^2, which is a 20 log10 L reduction (12 dB for L = 4). With the codebook keeping adjacent sector gains within δ of the peak (δ = 3 dB in the simulations), the selection is sensitive to noise at moderate SNR. The achievable rates in Fig. 10 are therefore upper bounds unless the operating SNR is high enough that the selection error is negligible. The paper should characterize the probability of correct selection as a function of the training SNR, or explicitly state the SNR regime for which the single-shot claim is intended.
[IV-C, Eqs. (48)–(49)] The codebook design and the coverage guarantee are based on the post-training gain GADMA(ϕ, fk*) in Eq. (49), where all Nz DMAs are reconfigured to the estimated frequency. However, the actual selection in Eq. (48) is performed on the training-stage gain of the L-sector sub-array configuration, which includes contributions from all L sectors (with only Q subarrays near the true angle). The paper does not prove that the subcarrier maximizing the training gain also ensures that the post-training gain is within δ of the maximum; the equality between the two objectives is not self-evident. The numerical examples in Figs. 8 and 9 verify the claim for two specific configurations, but the analytical statement in Section IV-C that the proposed codebook guarantees the δ-threshold for all angles is not connected to the selection rule. This should either be proven or explicitly presented as a numerically validated design.

minor comments (4)

[IV-B, Eq. (48)] The receiver is described as computing k* = argmax_k GADMA(ϕ, fk), but a receiver can only measure received power, not gain. Please state explicitly that the transmit power spectral density is equalized across subcarriers (or that the receiver normalizes by the known PSD and free-space path loss), so that the argmax of the measured power coincides with Eq. (48).
[III-B, Lemma 2] In Lemma 2, the case where p* is not an integer is solved numerically; this leaves a small gap in the 'closed-form' claim for the optimal operating frequency in that regime. It would help to state that the numerical maximization is a one-dimensional search over a bounded interval.
[IV-C, Lemma 5] The values of Ψδ(Ny) are quoted as approximately 0.448/Ny for δ = 0.5; please provide the numerical procedure or a reference for this constant, since it enters the codebook design.
[References] Reference [2] is cited as 'submitted' for a core modeling assumption about the coupling between bandwidth and frequency selectivity; please update the citation if the work has been published, or note that it is a preprint.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the N^2 gain and operating-frequency results are derived analytically from the Lorentzian model, and the codebook delta check is a design self-check rather than a disguised fit.

full rationale

The central derivation chain is self-contained. Starting from the Lorentzian polarizability model in Eq. (1), the paper rewrites the DMA weight using the algebraic shift-of-origin identity in Eq. (20), solves the frequency-selective gain problem in closed form in Lemma 1, and then optimizes the operating frequency in Lemma 2. Lemma 2 is an analytic consequence of |S(phi,ft)| <= N, so the N^2 TTD-equal gain is a mathematical bound, not a fitted result. Lemma 3 constructively designs n_g and d_y to keep p* integral over a desired angular range; it does not assume the conclusion it seeks. Lemma 4 solves a quadratic for the Lorentzian cutoff frequencies and is numerically checked in Fig. 3 with the same model parameters; this is consistency verification, not circularity. The beam-training codebook is designed from Lemma 5 to meet the threshold delta, and Fig. 9 confirms that the design criterion is met; this is a self-consistency/design check rather than an independent prediction, and the paper does not present it as a separately fitted parameter. The main unresolved limitation is that Eq. (48) assumes noiseless subcarrier selection while the training stage uses only Q = Nz/L subarrays, making the training pilot SNR roughly L^2 lower than the data stage; this is an unmodeled correctness risk, not a circular reduction. Self-citations [2] and [9] are background/motivation and a directly verifiable algebraic transformation, so they are not load-bearing. Overall, no significant circularity is present; the score reflects only minor self-citation.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central results are derived from the Lorentzian DMA model in [4] with added assumptions of no attenuation, a freely selectable per-user operating frequency, and noiseless beam training; these assumptions, plus the chosen parameters Gamma, p*, L, and delta, set the scope of the claims.

free parameters (5)

Damping factor Gamma = 2*pi*fc/50 ≈ 1.885 GHz for fc=15 GHz
Simulation parameter for the Lorentzian polarizability; controls the 3 dB bandwidth B ≈ Gamma/(2*pi) = 300 MHz. Not fitted to data but chosen per unit-cell design.
Natural index p* = 1
Chosen in simulations to avoid grating lobes; determines optimal spacing d_y* and the operating frequency mapping. The paper recommends a reasonable choice.
Number of training sectors L = 4
Design parameter for the codebook; the paper shows L=4 gives a 3 dB gain threshold for the specific N_y, N_z.
Gain threshold delta = 3 dB (and 0.6 dB in one example)
Codebook coverage threshold; determines the number of sectors via recursion.
Communication bandwidth B = varied up to 500 MHz
Simulation sweep to show rate versus bandwidth; the 300 MHz 3 dB bandwidth is the key crossover.

assumptions (5)

domain assumption DMA radiating slots follow the Lorentzian magnetic polarizability model (Eq. (1)) with fixed coupling factor F and damping Gamma.
Taken from Smith et al. [4]; the entire gain and bandwidth analysis is built on this coupled magnitude-phase model.
domain assumption The waveguide is lossless, so the excitation amplitude is uniform across all slots (Section II-A).
Used to derive the closed-form resonant frequencies; non-ideal attenuation is only evaluated later as a gap.
domain assumption The propagation channel is line-of-sight far-field with a single isotropic receive antenna (Section II-B).
The channel phase is a plane wave at angle phi; multipath is listed as future work.
domain assumption The transmitter can choose any operating frequency f_t in [f_t,min, f_t,max] per user (Section II-C).
Motivated by dynamic spectrum sharing and adaptive frequency hopping; this freedom is what the second-stage optimization exploits.
domain assumption Beam training can identify the best subcarrier index without noise or feedback error (Eq. (48)).
The training analysis ignores SNR variations and measurement noise, which is optimistic for the single-shot claim.

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Cite this review

Pith. "Pith review of Frequency-selective beamforming and single-shot beam training with dynamic metasurface antennas." pith.science (2026). https://pith.science/paper/NJRC3IKK

@misc{pith2026241200215,
  author       = {Pith},
  title        = {Pith review of: Frequency-selective beamforming and single-shot beam training with dynamic metasurface antennas},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NJRC3IKK}},
  note         = {Machine review of arXiv:2412.00215}
}

read the original abstract

Dynamic metasurface antennas (DMAs) beamform through low-powered components that enable reconfiguration of each radiating element. Previous research on a single-user multiple-input-single-output (MISO) system with a dynamic metasurface antenna at the transmitter has focused on maximizing the beamforming gain at a fixed operating frequency. The DMA, however, has a frequency-selective response that leads to magnitude degradation for frequencies away from the resonant frequency of each element. This causes reduction in beamforming gain if the DMA only operates at a fixed frequency. We exploit the frequency reconfigurability of the DMA to dynamically optimize both the operating frequency and the element configuration, maximizing the beamforming gain. We leverage this approach to develop a single-shot beam training procedure using a DMA sub-array architecture that estimates the receiver's angular direction with a single OFDM pilot signal. We evaluate the beamforming gain performance of the DMA array using the receiver's angular direction estimate obtained from beam training. Our results show that it is sufficient to use a limited number of resonant frequency states to do both beam training and beamforming instead of using an infinite resolution DMA beamformer.

Figures

Figures reproduced from arXiv: 2412.00215 by the authors.

**Figure 1.** A DMA has radiating slots which are tuned using low-power reconfigurable components with signal incident from one end of the waveguide. We leverage the frequency reconfigurability property of DMA for frequency-selective beamforming. symbol. We compare the beamforming gain for two cases: (i) perfectly known receiver’s angular direction, and (ii) angular direction estimated via the proposed training. We demonstrate th… view at source ↗

**Figure 2.** The DMA maximum gain angular range from broadside is plotted as a function of the fractional operating band for different refractive indices. The maximum beamforming angle can be improved using wider operating band and higher refractive index. d ⋆ y = cp⋆ (ft,max−ft,min) 2 sin(ϕmax)ft,minft,max . From Case 1 of Lemma 2, we see that the maximum value of beamforming gain N2 is obtained when the value of p ⋆ is chosen… view at source ↗

**Figure 3.** The beamforming gain of the DMA configured to maximize gain at ϕ = −18◦ and f ⋆ t (−18◦ ) = 16.43 GHz is shown as a function of frequency. The gain decreases for frequencies away from the optimal operating frequency. The 3 dB bandwidth of the beamforming gain frequency response is proportional to the damping factor. The frequency response of Gelement(ϕ, f) has sharp degradation with frequency compared to Garray(ϕ, … view at source ↗

Figures from the paper (7 more)

**Figure 5.** Figure 5: The optimal operating frequency from our proposed approach is shown as a function of the AoD. A monotonic behavior for the desired angular range ±30◦ is observed which corresponds to the maximum gain in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: An array of Nz DMAs where each DMA has Ny radiating slots. The sub-array approach enables simultaneous probing of different directions using different frequencies to estimate the optimal resonant frequency. Post beam training, all elements of the DMA array are configu…

**Figure 7.** Figure 7: The proposed beam codebook design ensures that the beamforming gain for all angles in the desired range is within a factor of δ from the maximum where δ is a design parameter. (a) Beam training with Ny = 8, Nz = 4, δ = 3 dB (b) Beam training with Ny = 4, Nz = 8, δ = 0…

**Figure 8.** Figure 8: The optimal operating frequency decreases in L = 4 steps as a function of the actual angle similar to the monotonic decreasing trend obtained for the continuous curve in [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: Beamforming gain at the optimal operating frequency obtained using angle estimate depends on Ny and Nz whereas beamforming gain with perfect angle depends solely on the product NyNz [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

**Figure 10.** Figure 10: The achievable rate DMA performance is comparable to the TTD performance up to a bandwidth of 300 MHz which is also the 3 dB DMA bandwidth as shown in [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 11.** Figure 11: DMA achievable rate based on optimal operating frequency approach is lower bounded by the DMA rate based on fixed frequency and upper bounded by the TTD rate for all tuning ranges. approach. We also showed in [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]

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P. T. Bowen, M. Boyarsky, L. M. Pulido-Mancera, D. R. Smith, O. Yurduseven, and M. Sazegar, “Optimizing polarizability distributions for metasurface apertures with lorentzian-constrained radiators,” arXiv preprint arXiv:2205.02747, 2022

2022 arXiv
[26]

A wideband generalization of the near-field region for extremely large phased-arrays,

N. Deshpande, M. R. Castellanos, S. R. Khosravirad, J. Du, H. Viswanathan, and R. W. Heath, “A wideband generalization of the near-field region for extremely large phased-arrays,” IEEE Wireless Commun. Lett., vol. 12, no. 3, pp. 515–519, 2023

2023
[27]

A high-efficiency reconfigurable element for dynamic metasurface antenna,

M. Lin, X. Huang, B. Deng, J. Zhang, D. Guan, D. Yu, and Y . Qin, “A high-efficiency reconfigurable element for dynamic metasurface antenna,” IEEE Access, vol. 8, pp. 87 446–87 455, May 2020

2020
[28]

Reconfigurable intelligent surface: Power consumption modeling and practical measurement validation,

J. Wang, W. Tang, J. C. Liang, L. Zhang, J. Y . Dai, X. Li, S. Jin, Q. Cheng, and T. J. Cui, “Reconfigurable intelligent surface: Power consumption modeling and practical measurement validation,” IEEE Trans. Commun., Mar. 2024

2024
[29]

Design and evaluation of reconfigurable intelligent surfaces in real- world environment,

G. C. Trichopoulos, P. Theofanopoulos, B. Kashyap, A. Shekhawat, A. Modi, T. Osman, S. Kumar, A. Sengar, A. Chang, and A. Alkhateeb, “Design and evaluation of reconfigurable intelligent surfaces in real- world environment,” IEEE Open J. Commun. Soc. , vol. 3, pp. 462–474, Mar. 2022

2022
[30]

Inverse multipath fingerprinting for Millimeter wave V2I beam alignment,

V . Va, J. Choi, T. Shimizu, G. Bansal, and R. W. Heath, “Inverse multipath fingerprinting for Millimeter wave V2I beam alignment,”IEEE Trans. Veh. Technol., vol. 67, no. 5, pp. 4042–4058, 2018

2018
[31]

Performance evaluation of dynamic metasurface antennas: Impact of insertion losses and coupling,

P. Ram ´ırez-Espinosa, R. J. Williams, J. Yuan, and E. De Carvalho, “Performance evaluation of dynamic metasurface antennas: Impact of insertion losses and coupling,” in GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022, pp. 2493–2498

2022
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Achievable rate of a SISO system under wideband matching network constraints,

N. V . Deshpande, M. R. Castellanos, S. R. Khosravirad, J. Du, H. Viswanathan, and R. W. Heath, “Achievable rate of a SISO system under wideband matching network constraints,” in GLOBECOM 2023 - 2023 IEEE Global Communications Conference , 2023, pp. 7514–7519

2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [2]

Wideband dynamic metasurface antenna performance with practical design impairments,

J. Carlson, N. V . Deshpande, M. R. Castellanos, and R. W. Heath Jr, “Wideband dynamic metasurface antenna performance with practical design impairments,” submitted to IEEE Trans. Wireless. Commun., Nov 2024

work page 2024

[2] [1]

Dynamic metasurface antennas for 6G extreme massive MIMO communications,

N. Shlezinger, G. C. Alexandropoulos, M. F. Imani, Y . C. Eldar, and D. R. Smith, “Dynamic metasurface antennas for 6G extreme massive MIMO communications,” IEEE Wireless Commun. , vol. 28, no. 2, pp. 106–113, 2021

2021

[3] [3]

Electronically steered metasurface antenna,

M. Boyarsky, T. Sleasman, M. F. Imani, J. N. Gollub, and D. R. Smith, “Electronically steered metasurface antenna,” Scientific reports, vol. 11, no. 1, p. 4693, 2021

work page 2021

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Analysis of a waveguide-fed metasurface antenna,

D. R. Smith, O. Yurduseven, L. P. Mancera, P. Bowen, and N. B. Kundtz, “Analysis of a waveguide-fed metasurface antenna,” Physical Review Applied, vol. 8, no. 5, p. 054048, 2017

work page 2017

[5] [5]

Nonuniform true time delay precoding in wideband MISO systems,

N. V . Deshpande, M. R. Castellanos, and R. W. Heath Jr., “Nonuniform true time delay precoding in wideband MISO systems,” in Proc. 56th Asilomar Conf. Signals, Syst., Comput. , 2022, pp. 1–5

work page 2022

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Delay-phase precoding for wideband THz massive MIMO,

L. Dai, J. Tan, Z. Chen, and H. V . Poor, “Delay-phase precoding for wideband THz massive MIMO,” IEEE Trans. Wireless. Commun. , vol. 21, no. 9, pp. 7271–7286, 2022

work page 2022

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True time delay in phased arrays,

R. Rotman, M. Tur, and L. Yaron, “True time delay in phased arrays,” Proc. IEEE, vol. 104, no. 3, pp. 504–518, Mar. 2016

work page 2016

[8] [8]

Beam focusing for multi-user MIMO communications with dynamic metasurface antennas,

H. Zhang, N. Shlezinger, F. Guidi, D. Dardari, M. F. Imani, and Y . C. Eldar, “Beam focusing for multi-user MIMO communications with dynamic metasurface antennas,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 4780–4784

work page 2021